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Real-time monitoring of intracranial pressure (ICP) is a routine part of neurocritical care in the management of brain injury. While mainly used to detect episodes of intracranial hypertension, the ICP signal is also indicative of the volume-pressure relationship within the cerebrospinal system, often referred to as intracranial compliance (ICC). Several ICP signal descriptors have been proposed in the literature as surrogates of ICC, but the possibilities of combining these are still unexplored. In the present study, a rapid ICC assessment consisting of a 30-degree postural shift was performed on a cohort of 54 brain-injured patients. 73 ICP signal features were calculated over the 20 minutes prior to the ICC test. After a selection step, different combinations of these features were provided as inputs to classification models. The goal was to predict the level of induced ICP elevation, which was categorized into three classes: less than 7 mmHg ("good ICC"), between 7 and 10 mmHg ("medium ICC"), and more than 10 mmHg ("poor ICC"). A logistic regression model fed with a combination of 5 ICP signal features discriminated the "poor ICC" class with an area under the receiving operator curve (AUROC) of 0.80 (95%-CI: [0.73-0.87]). The overall one-versus-one classification task was achieved with an averaged AUROC of 0.72 (95%-CI: [0.61-0.83]). Adding more features to the input set and/or using nonlinear machine learning algorithms did not significantly improve classification performance. This study highlights the potential value of analyzing the ICP signal independently to extract information about ICC status. At the patient's bedside, such univariate signal analysis could be implemented without dependence on a specific setup., Competing Interests: Marion Prud’homme and Donatien Legé are employees of Sophysa Company. Laurent Gergelé has performed consulting work for Sophysa Company. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2024 Legé et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Wetlands perform important functions and provide essential ecological services, including flood attenuation, groundwater recharge and discharge, and water purification. Human activities such as urban and rural development, drainage, and land alteration can cause major disturbances, often resulting in the drying up of wetlands. Therefore, many restoration projects aim to restore wetland hydrology. Hydrology significantly affects wetland functions by modifying and determining the wetlands physicochemical environment that allows for the development of a specific biota. Despite the importance of hydrology, monitoring efforts are mainly focused on surveying and characterizing wetland habitats or plant composition. There are few datasets available from the monitoring of the depth to water table (DTW) in wetlands and when available they are rarely shared. Collecting hydrological data can contribute to a better understanding of the relationship between hydrology, soil and habitat and can help understand the effect of climate change. From 2021 to 2023, depth to water table, soil and habitat data were collected across a variety of wetland types in France with a focus on hydrological data. Using data loggers placed in 37 monitoring wells across 17 wetlands, 469001 hourly depth to water table and water temperature data were collected. The dataset includes two files containing a total of 22 variables that describe the location of sites, habitat (EUNIS: European Nature Information System), soil hydromorphy, depth to water table, and water temperature. The dataset can be used to better understand wetland hydrology and its relationship to soil and habitat. The data collection process may be used to help restoration project achieve their goal., (© 2024 The Author(s).)

Background and Objective: Activated clotting time (ACT) is a point-of-care test used to monitor the effect of unfractionated heparin (UFH) during cardiopulmonary bypass (CPB). This test sometimes returns aberrant values, which can lead to the administration of an inappropriate dosing regimen. The development of a population-robust K-PD model of UFH could allow the individualisation and automation of UFH therapy during CPB., Methods: We conducted a prospective observational study to collect ACT measurements from patients undergoing surgery using CPB. The ACT data were split into a development and validation cohort. The development cohort was used to estimate a standard and robust population K-PD model characterised by a residual error following a normal distribution and student's t-distribution. The ACT prediction performance using Bayesian estimates of individual K-PD parameters was evaluated by comparing predicted versus observed ACTs. Using estimates of the robust K-PD model, a Bayesian individualisation strategy to automate UFH administration was proposed and evaluated using Monte Carlo simulations., Results: A total of 295 patients were included in the study, and 1561 ACTs were collected. In patients without outlier values, Bayesian estimates (based on four ACT measurements) from both standard and robust K-PD models had similar performances, with a median prediction bias close to 0 s. In patients with outlier measurements, the use of the robust K-PD model greatly improved the prediction bias and root-mean-square error (RMSE), with a mean prediction bias of 3.25 s, IQR = [-19.9; 46.03] versus -86 s IQR = [-135.7; -63.8] for the standard model. Monte Carlo simulations showed that the robust Bayesian individualisation strategy allowed the ACT to be maintained above the target using only two to three ACT measurements., Conclusions: The use of a robust K-PD model reduced prediction bias and RMSE in patients with outlier ACT measurements. The Bayesian individualisation strategy using robust estimates of individual parameters may help automate UFH dosing regimens. Proper clinical validation is warranted before its use in daily clinical practice., Competing Interests: Declaration of Competing Interest There are no competing interests to declare., (Copyright © 2021 Elsevier B.V. All rights reserved.)